We use Anaconda for data science model and development, specifically for coding in Python. We use it mainly for forecasting and predicting models within the environment of Anaconda Python.
I have been very enthusiastic about artificial intelligence and machine learning since my first year. I started learning Python in my first year and was using a MacBook with the M1 chip, which didn't have native Python support. I discovered Anaconda, which developed Python for Mac, so I started using it for Python. Later, I realized its use cases in machine learning and data science.
Analytics Analyst at a tech services company with 10,001+ employees
Real User
2020-08-13T08:33:00Z
Aug 13, 2020
In Anaconda, we get everything: RStudio, Spyder, and Jupyter. R Studio is for R, and Spyder and Jupyter are for Python. Using these, we will be doing data wrangling and data modeling for a developing project.
l began using it because it was open source and it was free and I knew other people who were using it. I just installed it and I got on with my testing. It was very useful for me because I could save my coding and present it to my assessor.
Head - Data Science (Senior Program Manager) at a tech services company with 51-200 employees
Real User
2019-12-16T08:14:00Z
Dec 16, 2019
We use different data science platforms for customer-specific projects. Whatever is being requested by, or is required by the customer, we learn it. Python is one of the technologies that we have a lot of experience with, and it is part of Anaconda. Our primary use case is analytics. We use Anaconda to build models that predict the probability of an event, or it can be used for classification purposes. There are various uses for this tool. One of the things that we do is subrogation and I can explain by using the example of a car accident. When an accident happens, you take your car to your insurance company and give them details about what happened. Also, the advisor at a service center will write down relevant information and supply it to the insurance company as well. At this point, the insurance company reimburses expenses for all of the damages that you have incurred. At the same time, they would like to find out if there is any fault that can be attributed to another person. If so, then they want to know whether it is possible to make any kind of recovery from that person or their insurance company. With thousands of these claims coming into the insurance companies, it is very difficult for somebody to read all of the information and decide whether there is a potential for recovery or not. This is where our application comes into effect. We read all of the data into our software, which is built with Python using Anaconda, and try to gain an understanding of each and every case. This includes many details, even claim history, and we try to assess what the chances are of recovery or what the chances are of subrogation in each case. This is just an example from one of our several clients. Each customer has different requirements and we customize a solution based on their needs.
Master Data at a energy/utilities company with 1,001-5,000 employees
Real User
2019-12-09T10:58:00Z
Dec 9, 2019
My position is master of data and we are a customer of Anaconda. Our primary use case was to find technological solutions to manage our warehouse in conjunction with our customer base. Anaconda enabled me to plot the data on a graph and find the optimal area for where our warehouse should be located.
Assistant Professor at Veermata Jijabai Technological Institute (VJTI)
Real User
2019-01-17T07:50:00Z
Jan 17, 2019
The best platform for a data scientist for development purposes. It supports applications which are needed for data analytics like Jupiter and predictive analytics like R.
Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination...
We use Anaconda for data science model and development, specifically for coding in Python. We use it mainly for forecasting and predicting models within the environment of Anaconda Python.
I have been very enthusiastic about artificial intelligence and machine learning since my first year. I started learning Python in my first year and was using a MacBook with the M1 chip, which didn't have native Python support. I discovered Anaconda, which developed Python for Mac, so I started using it for Python. Later, I realized its use cases in machine learning and data science.
I use the tool for Jupyter Notebook.
In Anaconda, we get everything: RStudio, Spyder, and Jupyter. R Studio is for R, and Spyder and Jupyter are for Python. Using these, we will be doing data wrangling and data modeling for a developing project.
l began using it because it was open source and it was free and I knew other people who were using it. I just installed it and I got on with my testing. It was very useful for me because I could save my coding and present it to my assessor.
We primarily use the solution for the data science class. It's used for people to build their data science models.
I was using this solution for buildings some PoCs, as well as during a hackathon.
I use the solution for learning purposes only. I don't use it for any production standard quota, and have not deployed it.
We use different data science platforms for customer-specific projects. Whatever is being requested by, or is required by the customer, we learn it. Python is one of the technologies that we have a lot of experience with, and it is part of Anaconda. Our primary use case is analytics. We use Anaconda to build models that predict the probability of an event, or it can be used for classification purposes. There are various uses for this tool. One of the things that we do is subrogation and I can explain by using the example of a car accident. When an accident happens, you take your car to your insurance company and give them details about what happened. Also, the advisor at a service center will write down relevant information and supply it to the insurance company as well. At this point, the insurance company reimburses expenses for all of the damages that you have incurred. At the same time, they would like to find out if there is any fault that can be attributed to another person. If so, then they want to know whether it is possible to make any kind of recovery from that person or their insurance company. With thousands of these claims coming into the insurance companies, it is very difficult for somebody to read all of the information and decide whether there is a potential for recovery or not. This is where our application comes into effect. We read all of the data into our software, which is built with Python using Anaconda, and try to gain an understanding of each and every case. This includes many details, even claim history, and we try to assess what the chances are of recovery or what the chances are of subrogation in each case. This is just an example from one of our several clients. Each customer has different requirements and we customize a solution based on their needs.
I use this solution for some of my assignments. Basically, it is used to take data from our database, analyze it, and make predictions.
We use Anaconda to develop machine learning models. Use primarily use Scikit-learn and TensorFlow.
We primarily use the solution for deep learning and machine learning.
My position is master of data and we are a customer of Anaconda. Our primary use case was to find technological solutions to manage our warehouse in conjunction with our customer base. Anaconda enabled me to plot the data on a graph and find the optimal area for where our warehouse should be located.
The best platform for a data scientist for development purposes. It supports applications which are needed for data analytics like Jupiter and predictive analytics like R.